Maximizing the number of served users while maintaining an acceptable Quality of Service (QoS) is always a serious challenge in the operation of cellular networks. Considerable effort has been excerpted by standards groups of 3GPP as well as equipment vendors research teams, chip makers, operations research groups, and network operators to handle this challenge. The capacity challenge in cellular networks stems from various sources depending on technology and access techniques.
In broadband 3G standards deployed by Code Division Multiple Access (CDMA) and Universal Mobile Telecommunications Service (UMTS) cellular networks, the main factors affecting capacity are power, interference, and processing hardware elements. The real challenge in such systems is to control downlink and uplink power consumption in order to a) ensure base stations do not run out of power when feeding downlink power amplifiers, and b) prevent noise rise on uplinks causing access failures. Considerable research has been conducted on power control algorithms to address the challenge [8,9]. Such efforts have mostly focused on mobile handsets, Base Transceiver Stations (BTS) and UMTS Base Stations (NodeB), Base Station Controllers (BSC), and Radio Network Controllers (RNC) software that make power control decisions. However, most of these algorithms focus on the behavior of individual elements and the associated impact on quality and capacity of individual elements.
In practice, network operators concern themselves to make sure a cluster of sites serving a specific geographical area such as a large urban downtown are performing well when delivering proper capacity and quality to mobile users. In order to optimize the operation of a cluster of cells, traffic has to be offloaded from more congested cells to less congested cells. 3G traffic offloading can be achieved in several ways such as changing the Common Control Pilot CHannel (CPiCH) power of a cell i referred to as Ωi, tilt of antennas, azimuth of cells, handover thresholds, etc. [10,11]. In reality, such changes may be effective for static network configurations relying on manual changes made by network engineers monitoring and assessing the impact. Further, some changes such as changing azimuth or tilt of the antennas take effect slowly and are yet very costly. They also require proper knowledge of the location of users such that the change does not have a negative impact on user coverage. To that end, some attempts were made to change Ωi power and tilt [8], [9] while others utilized Cell Individual Offset (CIO) of a cell i referred to as Φi [12] to redistribute traffic.
For the foregoing reasons, there is a need to address above-mentioned capacity challenge in 3G cellular networks to dynamically handle traffic distribution according to demand. The main focus of this invention is predicting capacity limits, i.e., breakpoints of cellular towers and providing a dynamic automated solution that significantly improves capacity.
The phrases cell, cell tower, and cellular tower are used interchangeably in the disclosure of this invention.